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Investigation of occurrence of diffusion instability in a set of three reaction–diffusion equations is carried out. In the general case the condition for both Turing and wave instabilities are obtained. Qualitative properties of the system, in which the bifurcation of each of the two types can take place, are clarified. In numerical experiments it is shown that if the corresponding conditions are met in the nonlinear model, spatiotemporal patterns are formed, which are predicted by linear analysis.

Necessary and sufficient conditions for the existence of solutions of nonlinear nonautonomous periodic problem for Hill’s equation in the case of parametric resonance. A characteristic feature of the task is the need of finding, as desired solution, and the corresponding eigenfunction, which ensures solvability of the periodic problem for Hill’s equation in the case of parametric resonance. To construct solutions of the periodic problem for Hill’s equation and the corresponding eigenfunction in the case of parametric resonance proposed iterative scheme, based on the method of simple iterations with used list-square technics.

The algorithm of fuzzy control system essentially nonlinear dynamic object is considered in this article. For solving nonlinear optimal control problem is proposed to use the method of linear quadratic regulation (LQR) with fuzzy Takagi–Sugeno model. The algorithm can be used for the design of deterministic optimal control of nonlinear objects. The algorithm of optimal control for controlling the rotational motion of a space vehicle is proposed.

The paper provides a solution of a task of calculating the parameters of a Rician distributed signal on the basis of the maximum likelihood principle in limiting cases of large and small values of the signal-tonoise ratio. The analytical formulas are obtained for the solution of the maximum likelihood equations’ system for the required signal and noise parameters for both the one-parameter approximation, when only one parameter is being calculated on the assumption that the second one is known a-priori, and for the two-parameter task, when both parameters are a-priori unknown. The direct calculation of required signal and noise parameters by formulas allows escaping the necessity of time resource consuming numerical solving the nonlinear equations’ s system and thus optimizing the duration of computer processing of signals and images. There are presented the results of computer simulation of a task confirming the theoretical conclusions. The task is meaningful for the purposes of Rician data processing, in particular, magnetic-resonance visualization.

We construct necessary and sufficient conditions for the existence of solution of seminonlinear matrix boundary value problem for a parametric excitation system of ordinary differential equations. The convergent iteration algorithms for the construction of the solutions of the semi-nonlinear matrix boundary value problem for a parametric excitation system differential equations in the critical case have been found. Using the convergent iteration algorithms we expand solution of seminonlinear periodical boundary value problem for a parametric excitation Riccati type equation in the neighborhood of the generating solution. Estimates for the value of residual of the solutions of the seminonlinear periodical boundary value problem for a parametric excitation Riccati type equation are found.

The paper considers hereditarity oscillator which is characterized by oscillation equation with derivatives of fractional order $\beta$ and $\gamma$, which are defined in terms of Gerasimova-Caputo. Using Laplace transform were obtained analytical solutions and the Green’s function, which are determined through special functions of Mittag-Leffler and Wright generalized function. It is proved that for fixed values of $\beta = 2$ and $\gamma = 1$, the solution found becomes the classical solution for a harmonic oscillator. According to the obtained solutions were built calculated curves and the phase trajectories hereditarity oscillatory process. It was found that in the case of an external periodic influence on hereditarity oscillator may occur effects inherent in classical nonlinear oscillators.

A formal model mechanism of oscillon formation is proposed. These structures were found in a variety of physical systems and a chemical Belousov–Jabotinsky reaction proceeding in an aerosol OT water-inoil microemulsion. Via the proposed mechanism oscillons occur as a result of interaction of two subsystems. In the first subsystem for a proper set of parameters solitary stationary structures may arise as a result of hard local excitation. These structures influence spatial distribution of the second subsystem parameter that leads to local oscillations in the subsystem.

The paper provides a solution of the two-parameter task of joint signal and noise estimation at data analysis within the conditions of the Rice distribution by the techniques of mathematical statistics: the maximum likelihood method and the variants of the method of moments. The considered variants of the method of moments include the following techniques: the joint signal and noise estimation on the basis of measuring the 2-nd and the 4-th moments (MM24) and on the basis of measuring the 1-st and the 2-nd moments (MM12). For each of the elaborated methods the explicit equations’ systems have been obtained for required parameters of the signal and noise. An important mathematical result of the investigation consists in the fact that the solution of the system of two nonlinear equations with two variables — the sought for signal and noise parameters — has been reduced to the solution of just one equation with one unknown quantity what is important from the view point of both the theoretical investigation of the proposed technique and its practical application, providing the possibility of essential decreasing the calculating resources required for the technique’s realization. The implemented theoretical analysis has resulted in an important practical conclusion: solving the two-parameter task does not lead to the increase of required numerical resources if compared with the one-parameter approximation. The task is meaningful for the purposes of the rician data processing, in particular — the image processing in the systems of magnetic-resonance visualization. The theoretical conclusions have been confirmed by the results of the numerical experiment.

We present the iterative algorithm that solves numerically both Urysohn type Fredholm and Volterra nonlinear one-dimensional nonsingular integral equations of the second kind to a specified, modest user-defined accuracy. The algorithm is based on descending recursive sequence of quadratures. Convergence of numerical scheme is guaranteed by fixed-point theorems. Picard’s method of integrating successive approximations is of great importance for the existence theory of integral equations but surprisingly very little appears on numerical algorithms for its direct implementation in the literature. We show that successive approximations method can be readily employed in numerical solution of integral equations. By that the quadrature algorithm is thoroughly designed. It is based on the explicit form of fifth-order embedded Runge–Kutta rule with adaptive step-size self-control. Since local error estimates may be cheaply obtained, continuous monitoring of the quadrature makes it possible to create very accurate automatic numerical schemes and to reduce considerably the main drawback of Picard iterations namely the extremely large amount of computations with increasing recursion depth. Our algorithm is organized so that as compared to most approaches the nonlinearity of integral equations does not induce any additional computational difficulties, it is very simple to apply and to make a program realization. Our algorithm exhibits some features of universality. First, it should be stressed that the method is as easy to apply to nonlinear as to linear equations of both Fredholm and Volterra kind. Second, the algorithm is equipped by stopping rules by which the calculations may to considerable extent be controlled automatically. A compact C++-code of described algorithm is presented. Our program realization is self-consistent: it demands no preliminary calculations, no external libraries and no additional memory is needed. Numerical examples are provided to show applicability, efficiency, robustness and accuracy of our approach.

A new type of cosmological models for the Universe that has no Beginning and evolves from the infinitely distant past is considered.

These models are alternative to the cosmological models based on the Big Bang theory according to which the Universe has a finite age and was formed from an initial singularity.

In our opinion, there are certain problems in the Big Bang theory that our cosmological models do not have.

In our cosmological models, the Universe evolves by compression from the infinitely distant past tending a finite minimum of distances between objects of the order of the Compton wavelength $\lambda_C$ of hadrons and the maximum density of matter corresponding to the hadron era of the Universe. Then it expands progressing through all the stages of evolution established by astronomical observations up to the era of inflation.

The material basis that sets the fundamental nature of the evolution of the Universe in the our cosmological models is a nonlinear Dirac spinor field $\psi(x^k)$ with nonlinearity in the Lagrangian of the field of type $\beta(\bar{\psi}\psi)^n$ ($\beta = const$, $n$ is a rational number), where $\psi(x^k)$ is the 4-component Dirac spinor, and $\psi$ is the conjugate spinor.

In addition to the spinor field $\psi$ in cosmological models, we have other components of matter in the form of an ideal liquid with the equation of state $p = w\varepsilon$ $(w = const)$ at different values of the coefficient $w (−1 < w < 1)$. Additional components affect the evolution of the Universe and all stages of evolution occur in accordance with established observation data. Here $p$ is the pressure, $\varepsilon = \rho c^2$ is the energy density, $\rho$ is the mass density, and $c$ is the speed of light in a vacuum.

We have shown that cosmological models with a nonlinear spinor field with a nonlinearity coefficient $n = 2$ are the closest to reality.

In this case, the nonlinear spinor field is described by the Dirac equation with cubic nonlinearity.

But this is the Ivanenko–Heisenberg nonlinear spinor equation which W.Heisenberg used to construct a unified spinor theory of matter.

It is an amazing coincidence that the same nonlinear spinor equation can be the basis for constructing a theory of two different fundamental objects of nature — the evolving Universe and physical matter.

The developments of the cosmological models are supplemented by their computer researches the results of which are presented graphically in the work.